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Should we worry about home bias in development research?

David McKenzie's picture
Paolo Abarcar recently tweeted that “a casual look at Econ job market papers reveals that people usually write about their home country”. This got me thinking about this phenomenon of “home bias” in development research, and whether it is something that warrants any thought, whether researchers should think about this in choosing their portfolio of projects, and implications for recruiting people into the profession.

First, how much of a home bias is there?
I thought I would put numbers to Paolo’s casual observation. The job market seems a reasonable place to start, since people have a designated job market paper, many people list citizenship on their C.V., or other features that can be used to determine citizenship, and it captures the country choices of students before they have found jobs. The latter point is important if the choice of country to work on affects the position these students end up receiving.

Looking at job market students at 20 top universities in the U.S., U.K. and France, I count 29 Ph.D. students on the economics job market who list development economics as one of their fields, and who are from a developing country.
  • Home bias is prevalent: 24 of the 29 students (83%) had a job market paper on their country of origin. Only Colombians were more likely to work on another country than their home country (4 out of 7 were working on other countries, such as Mexico and Peru).

Pros and Cons of this for Individual Researchers

Weekly links February 15: time to change your research production function? Hurray for big retailers and big data, but watch out for dynamic responses, and more....

David McKenzie's picture
  • This is the best thing I’ve read all week, particularly because it contrasts so much what my usual workflow looks like with what I would like more of it to look like – Cal Newport (of Deep Work fame) asks in the Chronicle Review “is email is making professors stupid?”. He notes that in the modern environment professors/researchers act more like middle managers than monks and suggests reforms to significantly restructure work culture to provide professors more uninterrupted time for thinking and teaching, and require less time on email and administrative duties. He gives the example of Donald Knuth, who does not have email and has an executive assistant who “intercepts all incoming communication, makes sense of it, brings to Knuth only what he needs to see, and does so only at ideal times for him to see it. His assistant also directly handles the administrative chores — things like scheduling meetings and filing expenses — that might otherwise add up to a major time sink for Knuth. It’s hard to overstate the benefits of this setup. Knuth is free to think hard about the most important and specialized aspects of his work, for hours at a time, disconnected from the background pull of inboxes”. It does make me think back to this old post I wrote on O-ring and Knowledge Hierarchy production functions for impact evaluations though, and the continued ability of O-ring issues to stymie my projects.

Now that I’ve noted that, here’s plenty of things to distract you from working deeper:

The Latest Evidence on Gender and Development

David Evans's picture

A new collection of papers – Towards Gender Equity in Development – sets out to “explore key sources of female empowerment and discuss the current challenges and opportunities for the future” in three categories: marriage, outside options, and laws and cultural norms. The final published book is available for free, and the individual chapters are available as working papers.

In the introduction, Anderson, Beaman, and Platteau discuss the current landscape of gender discrimination in low- and middle-income countries. In a set of tables that I’ve transformed into a single, completely unwieldy figure. We see discrimination in social norms, legal rights, and marriage indicators. (In all of these indicators, 100% is the worst; 0% is the best.) What stands out is that while no single region dominates the discrimination landscape, every region has significant room to improve.  West Africa has high rates of female genital mutilation, South Asia has high rates of son bias, Central Africa has high rates of polygyny, West Asia has high mobility restrictions on women, and the Caribbean has few to no laws against harassment.

Men at Work: Shhh!

Berk Ozler's picture

“Who can it be now?”

I turn my head around from my seat at the seminar table to see who it is this time that has interrupted the seminar speaker for the Nth time before she even got through her introductory slides: it was a man, of course. 

A lot of people at econ seminars get annoyed at questions that would have been answered naturally had the audience just been patient enough to wait for, sometimes literally, another slide; the back and forth that sometimes ensues between a questioner and the speaker; and, of course, the inevitable consequence of the speaker rushing through their results because too much time has been sent on answering questions.

Weekly links February 8: some people still like knowledge, be passionate about it, you don’t always need to make policy recommendations, and more...

David McKenzie's picture
  • Rachel Glennerster on lessons from a year as DFID’s Chief Economist, including the importance of knowledge work “As countries get richer, helping them spend their own money more effectively will become a more important route to reducing poverty than the UK directly paying for services”
  • Seema Jayachandran and Ben Olken offer their thoughts on new exciting areas in development research and advice for young development researchers: “taking the time to actually immerse yourself in the environments that you are studying. That means going to the countries that you’re studying and making sure that you understand the environment firsthand” and “not over-strategize about what topics or methods have career returns at the expense of not working on what you are personally most excited about.”
  • A reminder that not all research has to make policy recommendations: There is a new World Bank report on the mobility of displaced Syrians, which looks at the voluntary return decisions of over 100,000 refugees to understand key factors influencing these decisions, combined with simulations of how different security scenarios might influence voluntary returns. But I particularly liked this in the Q&A about the report “What policy recommendations do emerge from this report? This report does not aim to design policies. It focuses on informing such policies by providing the necessary data, analysis, and framework that demonstrate the tradeoffs between various policy choices.”  
  • Fabrizio Zilibotti on how inequality shapes parenting styles – next time your kids complain you are being too strict, you can blame the economic environment.

7 types of policy makers and what they mean for getting your research used

Markus Goldstein's picture
So you are out there with some results on a program that works and you really want to get your research used. And you’ve managed to schedule a meeting with a policy maker who is in a position to actually use your work. Maybe they even called you. As you start to discuss things with them, one key thing to think about is what that policy maker is looking for. Based on my experiences with this, there are seven types of policy makers, and knowing your counterpart’s type might be helpful in figuring out how to pitch your discussion:
 

Weekly links Feb 1: g big data, scaling up CCTs, “the data have been mined, of course”, and more...

David McKenzie's picture
  • Working with big datasets in Stata? Then the package gtools might be for you – I love that they have to give the caveat “Due to a Stata bug, gtools cannot support more than 2^31-1 (2.1 billion) observations”. Meanwhile, the Stata blog has the second post on doing power calculations via simulations in Stata.
  • More on industrial policy: A nice summary at VoxDev by Ernest Liu of his work on industrial policies in networks, and a reason to prioritize upstream sectors.
  • New SIEF note on using phone monitoring to help more money reach target beneficiaries: an example where small effects are meaningful when cheap and scaled to many people – the treatment group were only 1.3% more likely to get their money, but this meant about $1 million more funding reached farmers when officials knew they would be phone monitored, and the monitoring only cost $36,000.

Successful Teachers, Successful Students: A New Approach Paper on Teachers

David Evans's picture

Teachers are crucial to the learning process. Every year, we get new evidence from a new country on how much value an effective teacher adds. This is one area where the evidence lines up with intuition: Even without a bunch of value added measures, most of us would readily admit that without good teachers, we wouldn’t be where we are today. 

We’ve both done some research on teachers – Tara with her work on managing the teacher workforce in India, Dave with his work on teacher professional development, and both contributing to the World Bank’s World Development Report 2018 on learning. Over the last several months, we reviewed the latest evidence on how to attract the best candidates into the teacher profession and then how to prepare them, select them, support them, and motivate them. The result of that review is the new World Bank policy approach to teachers: Successful Teachers, Successful Students: Recruiting and Supporting Society’s Most Crucial Profession. We know this is a crowded field: There are lots of reports on how to help teachers to be as effective as they can. Our objective was to make the most recent evidence accessible, drawing on dozens of studies out in 2017 and 2018 as well as much of the accumulated work to that point.

Sex, Lies, and Measurement: Do Indirect Response Survey Methods Work? (No…)

Berk Ozler's picture

Smart people, mainly with good reason, like to make statements like “Measure what is important, don’t make important what you can measure,” or “Measure what we treasure and not treasure what we measure.” It is rumored that even Einstein weighed in on this by saying: “Not everything that can be counted counts and not everything that counts can be counted.” A variant of this has also become a rallying cry among those who are “anti-randomista,” to agitate against focusing research only on questions that one can answer experimentally.

However, I am confident that all researchers can generally agree that there is not much worse than the helpless feeling of not being able to vouch for the veracity of what you measured. We can deal with papers reporting null results, we can deal with messy or confusing stories, but what gives no satisfaction to anyone is to present some findings and then having to say: “This could all be wrong, because we’re not sure the respondents in our surveys are telling the truth.” This does not mean that research on sensitive topics does not get done, but like the proverbial sausage, it is necessary to block out where the data came from and how it was made.

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